Follow blockchain research across journals, conferences, and preprint repositories.
3,636 results · page 105 of 152
Gbenga Ibikunle, Frank McGroarty, Khaladdin Rzayev
No abstract is available for this record.
S. Alonso Monsalve, Andrés L. Suárez‐Cetrulo, Alejandro Cervantes, David Quintana
No abstract is available for this record.
Salim Lahmiri, Stelios Bekiros
No abstract is available for this record.
Ken Alabi
No abstract is available for this record.
Andrew Burnie, Emine Yılmaz, Tomaso Aste
The recent extreme volatility in cryptocurrency prices occurred in the setting of popular social media forums devoted to the discussion of cryptocurrencies. We develop a framework that discovers potential causes of phasic shifts in the price movement captured by social media discussions. This draws on principles developed in healthcare epidemiology where, similarly, only observational data are available. Such causes may have a major, one-off effect or recurring effects on the trend in the price series. We find a one-off effect of regulatory bans on bitcoin, the repeated effects of rival innovations on ether and the influence of technical traders, captured through discussion of market price, on both cryptocurrencies. The results for Bitcoin differ from Ethereum, which is consistent with the observed differences in the timing of the highest price and the price phases. This framework could be applied to a wide range of cryptocurrency price series where there exists a relevant social media text source. Identified causes with a recurring effect may have value in predictive modelling, whilst one-off causes may provide insight into unpredictable black swan events that can have a major impact on a system.
Samet Evci
Kripto para piyasası kısa dönemde çok hızlı bir gelişim göstermiş hem yatırımcıların hem de akademisyenlerin ilgisini çekmiştir. Bu piyasada en fazla piyasa değerine sahip kripto para birimi Bitcoin’dir. Gerek geleneksel finansal piyasaların işleyişinden farklı bir piyasa işleyişine sahip olması gerekse para yaratma sürecinde farklı bir sistemi kullanması yatırımcılar açısından Bitcoin fiyatlarında değişime yol açan faktörleri anlamayı gerekli kılmaktadır. Bu çalışma ile Bitcoin fiyatlarında haftanın günü anomalisinin varlığının araştırılması amaçlanmıştır. Bitcoin getirilerinde haftanın günü anomalisi, 2013-2019 yıllarına ait günlük fiyatlar kullanılarak asimetrik GARCH modeliyle incelenmiştir. Çalışmadan elde edilen bulgular Bitcoin getirileri üzerinde Pazartesi, Perşembe ve Pazar günlerinin negatif etkileri olduğunu ve en fazla kaybın Perşembe günü gerçekleştiğini ortaya koymuştur.
Monika di Angelo, Gernot Salzer
In the area of blockchains, a wallet is anything that manages the access to cryptocurrencies and tokens. Off-chain wallets appear in different forms, from paper wallets to hardware wallets to dedicated wallet apps, while on-chain wallets are realized as smart contracts. Wallet contracts are supposed to increase trust and security by being transparent and by offering features like daily limits, approvals, multiple signatures, and recovery mechanisms. The most prominent platform for smart contracts in general and the token ecosystem im particular, and thus also for wallet contracts is Ethereum. Our work aims at a better understanding of wallet contracts on Ethereum, since they are one of the most frequently deployed smart contracts. By analyzing source code, bytecode, and execution traces, we derive usage scenarios and patterns. We discuss methods for identifying wallet contracts in a semi-automatic manner by looking at the deployed bytecodes and the on-chain interaction patterns. We extract blueprints for wallets and compile a ground truth. Furthermore, we differentiate characteristics of wallets in use, and group them into six types. We provide numbers and temporal perspectives regarding the creation and use of wallets. For the 40 identified blueprints, we compile detailed profiles. We analyze the data of the Ethereum main chain up to block 11,500,000, mined on December 22, 2020.
Jan Lánský
Practical Applications Summary In Cryptocurrency Survival Analysis, from the Winter 2020 issue of The Journal of Alternative Investments, Jan Lansky (University of Finance and Administration) investigates the probabilities of individual cryptocurrencies being delisted. Although exchanges list more than 2,500 cryptocurrencies, a very high percentage of them ultimately will be delisted. Lansky provides background on cryptocurrencies, describes the data collection procedure, tallies live and dead cryptocurrencies, and shows how to calculate conditional probabilities of their demise. Most cryptocurrencies fail within five years of being listed on an exchange, yet the conditional probability of survival increases with the life of the cryptocurrency. For example, the probability of being delisted within one year is 35% for a new cryptocurrency, 27% after trading for one year, 19% after trading for two years, and 12% after three years. TOPICS:Currency, statistical methods, risk management, exchanges/markets/clearinghouses
Yufang Wang, Haiyan Wang
Over the past decade, the blockchain technology and its Bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for predicting bitcoin price. Through analysis of bitcoin subgraphs or chainlets, the PDE model captures the influence of transaction patterns on bitcoin price over time and combines the effect of all chainlet clusters. In addition, Google Trends Index is incorporated to the PDE model to reflect the effect of bitcoin market sentiment. The experiment shows that the average accuracy of daily bitcoin price prediction is 0.82 for 362 consecutive days in 2017. The results demonstrate the PDE model is capable of predicting bitcoin price. The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.
Nicola Uras, Lodovica Marchesi, Michele Marchesi, Roberto Tonelli
This paper studies how to forecast daily closing price series of Bitcoin,\nusing data on prices and volumes of prior days. Bitcoin price behaviour is\nstill largely unexplored, presenting new opportunities. We compared our results\nwith two modern works on Bitcoin prices forecasting and with a well-known\nrecent paper that uses Intel, National Bank shares and Microsoft daily NASDAQ\nclosing prices spanning a 3-year interval. We followed different approaches in\nparallel, implementing both statistical techniques and machine learning\nalgorithms. The SLR model for univariate series forecast uses only closing\nprices, whereas the MLR model for multivariate series uses both price and\nvolume data. We applied the ADF -Test to these series, which resulted to be\nindistinguishable from a random walk. We also used two artificial neural\nnetworks: MLP and LSTM. We then partitioned the dataset into shorter sequences,\nrepresenting different price regimes, obtaining best result using more than one\nprevious price, thus confirming our regime hypothesis. All the models were\nevaluated in terms of MAPE and relativeRMSE. They performed well, and were\noverall better than those obtained in the benchmarks. Based on the results, it\nwas possible to demonstrate the efficacy of the proposed methodology and its\ncontribution to the state-of-the-art.\n
Ying Chen, Paolo Giudici, Branka Hadji Misheva, Simon Trimborn
We aim to understand the dynamics of Bitcoin blockchain trading volumes and, specifically, how different trading groups, in different geographic areas, interact with each other. To achieve this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of trading volumes, both in time and in space. The extension is based on network models, which improve pure autoregressive models, introducing a contemporaneous contagion component that describes contagion effects between trading volumes. Our empirical findings show that transactions activities in bitcoins is dominated by groups of network participants in Europe and in the United States, consistent with the expectation that market interactions primarily take place in developed economies.
Christian Hafner
Alternative assets, defined by their low correlation with classical financial assets, have become an important investment vehicle in times of negative interest rates and in the aftermath of the global economic and financial crisis. Hedge funds increasingly invest in physical assets such as fine art, wine, or diamonds. Although digital and not physical, cryptocurrencies share many features of alternative assets, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This special issue covers a broad variety of topics in financial technology, and provides a state-of-the-art overview of cryptocurrencies from economic, financial, statistical and technical points of view.
Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Hana Sulieman
The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets.
Louis Bertucci
No abstract is available for this record.
Steven Pu, Makoto Yano
The Internet of things (IoT) Internet of Things (IoT) is considered a key driving force of what the JapaneseJapan government refers to as Society 5.0Society5.0, the image of an ideal future society that the JapaneseJapan government currently advocates. Society 5.0Society5.0 is defined as “a human-centered society that balances economic advancement with the resolution of social problems by a system that integrates cyberspace and physical spacePhysical space.” According to the government, “In Society 5.0Society5.0, a huge amount of information from sensorsSensors in physical spacePhysical space is accumulated in cyberspace.
Richard C. Gardner, Philipp Reinecke, Katinka Wolter
No abstract is available for this record.
Thibault Schrepel
No abstract is available for this record.
Brian Kachnowski
No abstract is available for this record.
Luiz Almeida Sampaio Filho
The behavior of the foreign exchange and cryptocurrency markets was studied from the perspective of the theory of dynamical systems. Using the phase space reconstruction procedure under the validity of Takens' theorem (1981). The presence of serial dependence was investigated through the BDS test, the property of sensitivity to initial conditions through the Lyapunov maximum exponent and the distinction between deterministic and stochastic signals observing the behavior of the E2(d) function in Cao's method (1997). Evaluating 17 exchange rate log-return series, evidence of serial dependence, possibly non-linear, was found in 11 of them. As for sensitivity to initial conditions, no series has shown conclusive results on such a property. All series presented evidence that they follow processes of a random nature and non-Gaussian increments, in the same way as the cryptocurrency log-return series. Of the 10 series of cryptocurrencies, the IID hypothesis was rejected for 8 of them, and none presented a conclusive result regarding a positive Lyapunov exponent. As a conclusion, no consistent characteristics of chaotic dynamics were found for the foreign exchange and digital currency markets in the analyzed period.
Antonis Ballis, Κωνσταντίνος Δράκος
No abstract is available for this record.
N. Shashidhar, Sourav Mahmood Sagar, Rachana Patil, Suraj Rk
To assess the Bitcoin cost absolutely considering divergent parameters that effect the Bitcoin esteem. In this work, we indicated grasp and recognize progressively changes in Bit Coin showcase while acquiring observation into most proper qualities encompassing Bitcoin cost. We anticipate the everyday value change with endorsing conceivable precision. The market finances of traded on an open market cryptographic forms of money at present above $230 billion. Bitcoin is most valuable cryptographic money, fills as an advanced store of significant worth, and its value consistency has been well-looked into. These attributes are appeared in the accompanying subdivision; the fundamental subtleties of Bitcoin
Ryuta Sakemoto
This study proposes a method to enhance cryptocurrency portfolios constructed by forecast models. This study forecasts returns on four liquid cryptocurrencies (Bitcoin, Litecoin, Ripple, and Dash) and determines the weights on the cryptocurrencies based upon a dynamic allocation framework. We assess the performances of the portfolios using the performance fee measure. Our results present that the proposed portfolios outperform the benchmark portfolio with the conventional level of the risk aversion parameter. The economic gain for an investor is equivalent to 12% per week. The economic gain is sensitive to a change in the risk aversion parameter, which contrasts with the studies of exchange rates which is due to the high volatility on the cryptocurrencies. Our predictors are related to the price momentum effects and they outperform widely used network factors.
Claude B. Erb
Bitcoin has been described as digital gold. Bitcoin is exactly like gold except when it isn’t. Over millennia, gold has gained a questionable reputation as an inflation hedge, a store of value and a safe haven. Gold’s price can arguably be decomposed into a “golden constant” fair price and a fair price deviation. Bitcoin has no track record as an inflation hedge, a store of value and a safe haven. Bitcoin’s price can arguably be decomposed into a questionable “bitcoin network” fair price and a fair price deviation. Both bitcoin and gold are about 50% above their “fair prices”.
Guglielmo Maria Caporale, Alex Plastun, Viktor Oliinyk
This paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in sample and out-of-sample performance of the various models are compared by means of appropriate selection criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).